
TensorFlow Federated (TFF).https://github.com/tensorflow/federated. TFF employs deep learning of neural networks to train FL models. It comprises two layers: Federated Learning and Federated Core. The former is a high-level interface that supports users to apply FL without the need for implementing FL algorithms personally. The latter enables users to implement and experiment new or customized FL algorithms. TFF supports the simulation of the distributed training of FL models but executes only on a single machine.

PySyft.https://github.com/OpenMined/PySyft It leverages deep learning, secure multiparty computation, and differential privacy to train privacy-preserving FL models in untrusted environments [1]}. The framework is based on PyTorch and provide a native Torch interface. Further, the framework simulates FL settings by creating clients as virtual workers and distributing data between them.

FATE.https://github.com/FederatedAI/FATE It is an open-source framework developed by Webank [2]}. It exploits secure computation protocols to train FL models using different algorithms, such as decision trees, logistic regression, transfer learning, and deep learning.

PaddleFL.https://github.com/PaddlePaddle/PaddleFL It is an open-source framework that supports the replication and comparison of FL algorithms as well as the deployment of FL systems in large scale distributed clusters.

TiFL. It is a Tier-based Federated Learning System that groups clients into tiers based on their training performance to mitigate the straggler problem caused by the heterogeneity of clients' capabilities or data quantity. TiFL supports also runtime adaptive grouping of clients into tiers based on the continuously monitored training performance [3]}.

